Task 5.2 aims to demonstrate how mobilisation of real-world population, health and care data across national borders can provide answers to policy-relevant research questions. Eventually, it aims to prototype a workflow that is standard for population health research. Here, the research question is approached by identifying a causal effect that allows to evaluate a public health intervention. As such, a methodology for approaching causal inference when conducting federated research is proposed and demonstrated, guaranteeing different layers of interoperability (i.e., legal, organisational, and semantic interoperability).
The methodological framework comprises the following steps:
- Defining the research question and the exposure-outcome relationship
- Establishing a causal model using Directed Acyclic Graphs (DAGs)
- Translating the causal model into data requirements using a Common Data Model (CDM)
- Generating synthetic data and developing an interoperable analytical pipeline
- Mobilising individual-level data within each of the nodes and transforming the data to comply with the CDM
- Deploying the interoperable analytical pipeline within the secure processing environment (SPE) of each of the nodes (Data Hubs)
- Meta-analysis of the aggregated results
The current use case aims to answer the following research question: "How effective have the SARS-CoV-2 vaccination programmes been in preventing SARS-CoV-2 infections?"
For more information, please consult the study protocol.
Space: BeYond-COVID (BY-COVID)
SEEK ID: https://workflowhub.eu/projects/159Funding codes:
Public web page: https://doi.org/10.5281/zenodo.7551181
WorkflowHub PALs: No PALs for this Team
Team start date: 13th Oct 2021
Team end date: 30th Oct 2024